Non-parametric policy gradients: A unified treatment of propositional and relational domains

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 456-463

  Kersting, Kristian ^a   Driessens, Kurt ^b  

^a FRAUNHOFER IAIS   (Germany)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

REGRESSION ANALYSIS; ROBOT LEARNING; LEARNING SYSTEMS;

CONTINUOUS DOMAINS; DO-MAINS; GRADIENT BOOSTING; POLICY GRADIENTS; REGRESSION MODELS; WEIGHTED SUMS; NON-PARAMETRIC;

LEARNING SYSTEMS; ROBOT LEARNING;

EID: 56449088242     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Aberdeen, D. (2006). Policy-gradient methods for planning. Advances in Neural Information Processing Systems 18 (pp. 9-17).
2. Bagnell, J. (2004). Learning decisions: Robustness, uncertainty, and approximation. Doctoral dissertation, Robotics Institute, Carnegie Mellon University, Pittsburg, Pa, USA.
3. Bagnell, J., & Schneider, J. (2003). Policy search in reproducing kernel hilbert space (Technical Report CMU-RI-TR-03-45). Robotics Institute, Carnegie Mellon University, Pittsburg, Pa, USA.
4. Baxter, J., Bartlett, P., & Weaver, L. (2001). Experiments with infinite-horizon, policy-gradient estimation. Journal of Arificial Intellifence Research (JAIR), 15, 351-381.
5. Bertsekas, D. P., & Tsitsiklis, J. (1996). Neurodynamic programming. Belmont, MA: Athena Scientific.
6. Blockeel, H., & De Raedt, L. (1998). Top-down induction of first order logical decision trees. Artificial Intelligence, 101, 285-297.
7. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Belmont: Wadsworth.
8. Chapman, D., & Kaelbling, L. P. (1991). Input generalization in delayed reinforcement learning: An algorithm and performance comparisions. Proceedings of the 12th International Joint Conference on Artificial Intelligence (pp. 726-731) Sydney, Australia.
9. Dietterich, T., Ashenfelter, A., & Bulatov, Y. (2004). Training conditional random fields via gradient tree boosting. Proceedings of the 21st International Conference on Machine Learning (pp. 217-224). Banff, Canada.
10. Driessens, K., & Džeroski, S. (2005). Combining model-based and instance-based learning for first order regression. Proceedings of the 22nd International Conference on Machine Learning (pp. 193-200) Bonn, Germany.
11. Driessens, K., & Dzeroski, S. (2004). Integrating guidance into relational reinforcement learning. Machine Learning, 57, 271-304.
12. Driessens, K., & Ramon, J. (2003). Relational instance based regression for relational reinforcement learning. Proceedings of the 20th International. Conference on Machine Learning (pp. 123-130) Washington, DC, USA.
13. Driessens, K., Ramon, J., & Blockeel, H. (2001). Speeding up relational reinforcement learning through the use of an incremental first order decision tree learner. Proceedings of the 12th European Conference on Machine Learning (pp. 97-108), Freiburg, Germany.
14. Dzeroski, S., De Raedt, L., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7-52.
15. Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research (JMLR), 6, 503-556.
16. Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189-1232.
17. Gärtner, T., Driessens, K., & Ramon, J. (2003). Graph kernels and gaussian processes for relational reinforcement learning. International Conference on Inductive Logic Programming (pp. 146-163) Szeged, Hungary.
18. Getoor, L., & Taskar, B. (2007). An introduction to statistical relational learning. MIT Press.
19. Guestrin, C., Lagoudakis, M., & Parr, R. (2002). Coordinated reinforcement learning. Proceedings of the 19th International Conference on Machine Learning (pp. 227-234) Sydney, Australia.
20. Gutmann, B., & Kersting, K. (2006). TildeCRF: Conditional random fields for logical sequences. Proceedings of the 17th European Conference on Machine Learning (pp. 174-185). Berlin, Germany.
21. Kersting, K., van Otterlo, M., & De Raedt, L. (2004). Bellman goes relational. Proceedings of the Twenty-First International Conference on Machine Learning (ICML-2004) (pp. 465-472). Banff, Canada.
22. Konda, V. R., & Tsitsiklis, J. N. (2003). On actor-critic algorithms. SIAM J. Control Optim., 42, 1143-1166.
23. McCallum, A. (1996). Reinforcement learning with selective perception and hidden state. Doctoral dissertation, University of Rochester, New York, NY, USA.
24. Munos, R. (2006). Policy gradient in continuous time. Journal of Machine Learning Research (JMLR), 7, 771-791.
25. Riedmiller, M. (2005). Neural fitted Q iteration - First experiences with a data efficient neural reinforcement learning method. Proceedings of the 16th European Conference on Machine Learning (pp. 317-328) Porto, Portugal.
26. Sanner, S., & Boutilier, C. (2005). Approximate linear programming for first-order MDPs. Proceedings of the 21st conference on Uncertainty in AI (UAI) (pp. 509-517) Edinburgh, Scotland.
27. Slaney, J., & Thiébaux, S. (2001). Blocks world revisited. Artificial Intelligence, 125, 119-153.
28. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: The MIT Press.
29. Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems 12 (pp. 1057-1063). MIT Press.
30. Uther, W., & Veloso, M. (1998). Tree based discretization for continuous state space reinforcement learning. Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-96) (pp. 769-774) Portland, OR, USA.
31. Wang, C., Joshi, S., & Khardon, R. (2007). First order decision diagrams for relational mdps. Proceedings of the 20th International Joint Conference on Artificial Intelligence (pp. 1095-1100). Hyderabad, India: AAAI press.
32. Wang, X., & Dietterich, T. (2003). Model-based policy gradient reinforcement learning. Proceedings of the 20th International Conference on Machine Learning (pp. 776-783) Washington, DC, USA.
33. Williams, R. (1992). Simple statistical gradient following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.